Development of a Multivariate Regression Model for Soil Nitrate Nitrogen Content Prediction
Bibliographic record
Abstract
Although soil nitrate nitrogen (N) is a nutrient source for crop, it could be a potential nonpoint pollution source to the environment when its content remains high with an inappropriate management. Soil nitrate N content is affected by various factors, such as cultivation practices, N fertilizer application rate, soil properties, and climatic conditions. Understanding the effects of these factors on soil nitrate N content is necessary for nitrogen management and nonpoint source pollution control. Taking the data measured from 1996 to 1998 in a 25 ha row crop field located in Central Iowa, this paper intended to study the interwoven effects of these factors on soil nitrate N content using multivariate statistical analysis techniques of sample mean plots, a multivariate analysis of variance (MANOVA) model, and a multivariate linear regression model. The inferences made by the sample mean plots and MANOVA model indicate that the effects of these factors are additive, i.e., their main or direct effects are statistically significant but the interaction effects between and among them are insignificant at a 5% significance level. Incorporating these additive effects, a multivariate linear regression model was fitted to the dataset. The residual plots show that the dataset follows an approximate bivariate normal distribution, which is assumed by the MANOVA and multivariate linear regression models. The validation using the field data collected in 1999 indicated that the model explained more than 93% variations exhibited by the measured sublayed-averaged data on soil nitrate N content and soil moisture. However, this model is unable to account for the within-sublayer variations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".